Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative meth...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/12/4195 |
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| author | Wan-Soo Kim Yong-Joo Kim Seung-Yun Baek Seung-Min Baek Yeon-Soo Kim Seong-Un Park |
| author_facet | Wan-Soo Kim Yong-Joo Kim Seung-Yun Baek Seung-Min Baek Yeon-Soo Kim Seong-Un Park |
| author_sort | Wan-Soo Kim |
| collection | DOAJ |
| description | In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R<sup>2</sup>) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R<sup>2</sup> of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction. |
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| format | Article |
| id | doaj.art-cbedb28aa3b94ddd86c3db957e11d6ee |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:03:35Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cbedb28aa3b94ddd86c3db957e11d6ee2023-11-20T04:18:00ZengMDPI AGApplied Sciences2076-34172020-06-011012419510.3390/app10124195Development of a Prediction Model for Tractor Axle Torque during Tillage OperationWan-Soo Kim0Yong-Joo Kim1Seung-Yun Baek2Seung-Min Baek3Yeon-Soo Kim4Seong-Un Park5Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, KoreaDepartment of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, KoreaResearch and Development Institute, Tongyang Moolsan Co. Ltd., Gongju 32530, KoreaIn general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R<sup>2</sup>) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R<sup>2</sup> of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction.https://www.mdpi.com/2076-3417/10/12/4195agricultural tractoraxle torqueprediction modelmultiple regressiontillage operation |
| spellingShingle | Wan-Soo Kim Yong-Joo Kim Seung-Yun Baek Seung-Min Baek Yeon-Soo Kim Seong-Un Park Development of a Prediction Model for Tractor Axle Torque during Tillage Operation Applied Sciences agricultural tractor axle torque prediction model multiple regression tillage operation |
| title | Development of a Prediction Model for Tractor Axle Torque during Tillage Operation |
| title_full | Development of a Prediction Model for Tractor Axle Torque during Tillage Operation |
| title_fullStr | Development of a Prediction Model for Tractor Axle Torque during Tillage Operation |
| title_full_unstemmed | Development of a Prediction Model for Tractor Axle Torque during Tillage Operation |
| title_short | Development of a Prediction Model for Tractor Axle Torque during Tillage Operation |
| title_sort | development of a prediction model for tractor axle torque during tillage operation |
| topic | agricultural tractor axle torque prediction model multiple regression tillage operation |
| url | https://www.mdpi.com/2076-3417/10/12/4195 |
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